Simple local environment descriptors for accurate prediction of hydrogen absorption and migration in metal alloys

16 August 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The control of hydrogen concentration and diffusion in metal alloys is critical for advancing clean energy technologies. A quantitative understanding of the composition-property relationship can accelerate the design of hydrogen storage materials, structural hydrogen-resistant alloys, and materials for energy-efficient nanoelectronics. In this work, we employed Density Functional Theory simulations to investigate the energy landscape of hydrogen absorption and mobility for a wide spectrum of high-entropy alloys and intermetallic compounds. Our study sheds light on the origin of hydrogen stability also providing quantitative guidance for compositional considerations and the design of technologically useful materials. The developed analytical model uses physically intuitive metrics characterizing the local environment, such as electronic structure features, volume of interstitial voids, and lattice vibrational modes, to predict the energy landscape of hydrogen in metal alloys based on simple calculations of bulk properties. The developed model exhibits remarkable accuracy in predicting hydrogen binding energy, achieving a low mean absolute error of less than 0.1 eV. Utilizing this model, we successfully predicted hydrogen absorption energies and migration barriers demonstrating that these quantities are linearly correlated, akin to the Brønsted−Evans−Polanyi principle. In addition, our study revealed that a similar list of material features can be employed to predict zero-point energy contribution associated with hydrogen vibrational motion.


Hydrogen Diffusion
Hydrogen Absorption
High Entropy Alloys
First Principles Calculations
Machine Learning

Supplementary materials

Supporting Information
Supporting Information contains following sections: 1. Descriptors Engineering and Descriptors description. 2. Mean Phonon center and mean metallic radius correlation. 3. SISSO dimensionallity optimization. 4. XGBoost model Performance and optimized parametres. 5. Feature and Permutation importance analysis. 6. SISSOZPE model performance for ZPE predictions in Transitional States. 7. Performance of the SISSODFT model in predicting absorption energy for the second with respect to descriptor changes. 8. Table for Linear Fitted Equations and R2 Values for Predicted Activation Energy and Absorption Energy Difference for Five FCC High-Entropy Alloys (HEA).


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